Overview

Dataset statistics

Number of variables21
Number of observations2263
Missing cells2202
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory745.7 B

Variable types

Numeric9
Categorical12

Alerts

CONSTITUENCY has a high cardinality: 539 distinct values High cardinality
NAME has a high cardinality: 2014 distinct values High cardinality
PARTY has a high cardinality: 133 distinct values High cardinality
SYMBOL has a high cardinality: 126 distinct values High cardinality
ASSETS has a high cardinality: 1979 distinct values High cardinality
LIABILITIES has a high cardinality: 1226 distinct values High cardinality
WINNER is highly correlated with GENERAL VOTES and 4 other fieldsHigh correlation
GENERAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
POSTAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
TOTAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL ELECTORS IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL VOTES POLLED IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
WINNER is highly correlated with GENERAL VOTES and 4 other fieldsHigh correlation
GENERAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
POSTAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
TOTAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL ELECTORS IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL VOTES POLLED IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
WINNER is highly correlated with GENERAL VOTES and 3 other fieldsHigh correlation
GENERAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
POSTAL VOTES is highly correlated with GENERAL VOTES and 3 other fieldsHigh correlation
TOTAL VOTES is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL ELECTORS IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
OVER TOTAL VOTES POLLED IN CONSTITUENCY is highly correlated with WINNER and 4 other fieldsHigh correlation
GRADUATE is highly correlated with EDUCATIONHigh correlation
EDUCATION is highly correlated with GRADUATEHigh correlation
Sl No: is highly correlated with STATEHigh correlation
STATE is highly correlated with Sl No: and 2 other fieldsHigh correlation
WINNER is highly correlated with GENERAL VOTES and 3 other fieldsHigh correlation
CATEGORY is highly correlated with STATEHigh correlation
EDUCATION is highly correlated with GRADUATEHigh correlation
GRADUATE is highly correlated with EDUCATIONHigh correlation
GENERAL VOTES is highly correlated with WINNER and 3 other fieldsHigh correlation
TOTAL VOTES is highly correlated with WINNER and 3 other fieldsHigh correlation
OVER TOTAL ELECTORS IN CONSTITUENCY is highly correlated with WINNER and 3 other fieldsHigh correlation
OVER TOTAL VOTES POLLED IN CONSTITUENCY is highly correlated with WINNER and 3 other fieldsHigh correlation
TOTAL ELECTORS is highly correlated with STATEHigh correlation
SYMBOL has 245 (10.8%) missing values Missing
GENDER has 245 (10.8%) missing values Missing
criminal has 245 (10.8%) missing values Missing
AGE has 245 (10.8%) missing values Missing
CATEGORY has 245 (10.8%) missing values Missing
EDUCATION has 245 (10.8%) missing values Missing
GRADUATE has 242 (10.7%) missing values Missing
ASSETS has 245 (10.8%) missing values Missing
LIABILITIES has 245 (10.8%) missing values Missing
criminal is highly skewed (γ1 = 24.8670552) Skewed
Sl No: is uniformly distributed Uniform
CONSTITUENCY is uniformly distributed Uniform
ASSETS is uniformly distributed Uniform
Sl No: has unique values Unique
OVER TOTAL ELECTORS IN CONSTITUENCY has unique values Unique
OVER TOTAL VOTES POLLED IN CONSTITUENCY has unique values Unique
criminal has 1264 (55.9%) zeros Zeros
POSTAL VOTES has 35 (1.5%) zeros Zeros

Reproduction

Analysis started2022-02-25 09:12:01.082374
Analysis finished2022-02-25 09:12:37.334696
Duration36.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Sl No:
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132
Minimum1
Maximum2263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:37.496805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile114.1
Q1566.5
median1132
Q31697.5
95-th percentile2149.9
Maximum2263
Range2262
Interquartile range (IQR)1131

Descriptive statistics

Standard deviation653.4161512
Coefficient of variation (CV)0.5772227484
Kurtosis-1.2
Mean1132
Median Absolute Deviation (MAD)566
Skewness0
Sum2561716
Variance426952.6667
MonotonicityStrictly increasing
2022-02-25T14:42:37.763983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
5471
 
< 0.1%
5351
 
< 0.1%
5371
 
< 0.1%
5391
 
< 0.1%
5411
 
< 0.1%
5431
 
< 0.1%
5451
 
< 0.1%
5491
 
< 0.1%
5311
 
< 0.1%
Other values (2253)2253
99.6%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
22631
< 0.1%
22621
< 0.1%
22611
< 0.1%
22601
< 0.1%
22591
< 0.1%
22581
< 0.1%
22571
< 0.1%
22561
< 0.1%
22551
< 0.1%
22541
< 0.1%

STATE
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size147.7 KiB
Uttar Pradesh
274 
Bihar
244 
Tamil Nadu
217 
West Bengal
193 
Maharashtra
192 
Other values (31)
1143 

Length

Max length25
Median length10
Mean length9.782589483
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTelangana
2nd rowTelangana
3rd rowTelangana
4th rowTelangana
5th rowUttar Pradesh

Common Values

ValueCountFrequency (%)
Uttar Pradesh274
12.1%
Bihar244
 
10.8%
Tamil Nadu217
 
9.6%
West Bengal193
 
8.5%
Maharashtra192
 
8.5%
Andhra Pradesh121
 
5.3%
Madhya Pradesh103
 
4.6%
Gujarat87
 
3.8%
Rajasthan86
 
3.8%
Odisha85
 
3.8%
Other values (26)661
29.2%

Length

2022-02-25T14:42:37.998142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh519
15.5%
uttar274
 
8.2%
bihar244
 
7.3%
tamil217
 
6.5%
nadu217
 
6.5%
west193
 
5.8%
bengal193
 
5.8%
maharashtra192
 
5.7%
andhra121
 
3.6%
madhya103
 
3.1%
Other values (38)1069
32.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CONSTITUENCY
Categorical

HIGH CARDINALITY
UNIFORM

Distinct539
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size145.9 KiB
AURANGABAD
 
14
GAYA (SC)
 
12
MAHARAJGANJ
 
9
UJIARPUR
 
9
SHEOHAR
 
8
Other values (534)
2211 

Length

Max length25
Median length8
Mean length8.945647371
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADILABAD
2nd rowADILABAD
3rd rowADILABAD
4th rowADILABAD
5th rowAGRA

Common Values

ValueCountFrequency (%)
AURANGABAD14
 
0.6%
GAYA (SC)12
 
0.5%
MAHARAJGANJ9
 
0.4%
UJIARPUR9
 
0.4%
SHEOHAR8
 
0.4%
JAHANABAD8
 
0.4%
SUPAUL8
 
0.4%
BARAMULLA8
 
0.4%
BASTAR8
 
0.4%
ARUKU8
 
0.4%
Other values (529)2171
95.9%

Length

2022-02-25T14:42:38.218286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sc50
 
1.9%
north35
 
1.3%
nagar31
 
1.2%
mumbai25
 
0.9%
east25
 
0.9%
south25
 
0.9%
west24
 
0.9%
delhi21
 
0.8%
21
 
0.8%
central18
 
0.7%
Other values (540)2384
89.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NAME
Categorical

HIGH CARDINALITY

Distinct2014
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size161.1 KiB
NOTA
245 
SURENDRA RAM
 
2
ATUL KUMAR SINGH
 
2
SANJAY KUMAR
 
2
Ajay Kumar
 
2
Other values (2009)
2010 

Length

Max length60
Median length15
Mean length15.82412726
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2008 ?
Unique (%)88.7%

Sample

1st rowSOYAM BAPU RAO
2nd rowGodam Nagesh
3rd rowRATHOD RAMESH
4th rowNOTA
5th rowSatyapal Singh Baghel

Common Values

ValueCountFrequency (%)
NOTA245
 
10.8%
SURENDRA RAM2
 
0.1%
ATUL KUMAR SINGH2
 
0.1%
SANJAY KUMAR2
 
0.1%
Ajay Kumar2
 
0.1%
Rahul Gandhi2
 
0.1%
Nityanand Rai1
 
< 0.1%
Sunil Paswan1
 
< 0.1%
Subhash Maharia1
 
< 0.1%
ALPHONS KANNANTHANAM1
 
< 0.1%
Other values (2004)2004
88.6%

Length

2022-02-25T14:42:38.468035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nota245
 
4.5%
singh210
 
3.9%
kumar163
 
3.0%
dr94
 
1.7%
ram46
 
0.8%
yadav45
 
0.8%
k37
 
0.7%
chandra36
 
0.7%
patel33
 
0.6%
prasad32
 
0.6%
Other values (2794)4497
82.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WINNER
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size128.3 KiB
0
1724 
1
539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01724
76.2%
1539
 
23.8%

Length

2022-02-25T14:42:38.725206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-25T14:42:38.846286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01724
76.2%
1539
 
23.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PARTY
Categorical

HIGH CARDINALITY

Distinct133
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size133.7 KiB
BJP
420 
INC
413 
NOTA
245 
IND
201 
BSP
163 
Other values (128)
821 

Length

Max length10
Median length3
Mean length3.424657534
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)2.7%

Sample

1st rowBJP
2nd rowTRS
3rd rowINC
4th rowNOTA
5th rowBJP

Common Values

ValueCountFrequency (%)
BJP420
18.6%
INC413
18.3%
NOTA245
10.8%
IND201
 
8.9%
BSP163
 
7.2%
CPI(M)100
 
4.4%
AITC47
 
2.1%
VBA47
 
2.1%
SP39
 
1.7%
NTK38
 
1.7%
Other values (123)550
24.3%

Length

2022-02-25T14:42:39.013398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bjp420
18.6%
inc413
18.3%
nota245
10.8%
ind201
 
8.9%
bsp163
 
7.2%
cpi(m100
 
4.4%
aitc47
 
2.1%
vba47
 
2.1%
sp39
 
1.7%
ntk38
 
1.7%
Other values (123)550
24.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SYMBOL
Categorical

HIGH CARDINALITY
MISSING

Distinct126
Distinct (%)6.2%
Missing245
Missing (%)10.8%
Memory size136.3 KiB
Lotus
420 
Hand
413 
Elephant
166 
Bicycle
 
65
Hammer, Sickle and Star
 
63
Other values (121)
891 

Length

Max length40
Median length5
Mean length8.239345887
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)1.9%

Sample

1st rowLotus
2nd rowCar
3rd rowHand
4th rowLotus
5th rowElephant

Common Values

ValueCountFrequency (%)
Lotus420
18.6%
Hand413
18.3%
Elephant166
 
7.3%
Bicycle65
 
2.9%
Hammer, Sickle and Star63
 
2.8%
Cup & Saucer52
 
2.3%
Flowers and Grass47
 
2.1%
Ganna Kisan45
 
2.0%
Battery Torch42
 
1.9%
Ears of Corn And Sickle37
 
1.6%
Other values (116)668
29.5%
(Missing)245
 
10.8%

Length

2022-02-25T14:42:39.265566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lotus420
 
13.5%
hand417
 
13.4%
and177
 
5.7%
elephant166
 
5.3%
sickle100
 
3.2%
69
 
2.2%
bicycle65
 
2.1%
star64
 
2.1%
hammer63
 
2.0%
kisan62
 
2.0%
Other values (164)1518
48.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GENDER
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing245
Missing (%)10.8%
Memory size128.5 KiB
MALE
1760 
FEMALE
258 

Length

Max length6
Median length4
Mean length4.255698712
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
MALE1760
77.8%
FEMALE258
 
11.4%
(Missing)245
 
10.8%

Length

2022-02-25T14:42:39.508521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-25T14:42:39.663212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male1760
87.2%
female258
 
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

criminal
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct28
Distinct (%)1.4%
Missing245
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean1.453914767
Minimum0
Maximum240
Zeros1264
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:39.843331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum240
Range240
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.636973124
Coefficient of variation (CV)5.252696579
Kurtosis718.7534801
Mean1.453914767
Median Absolute Deviation (MAD)0
Skewness24.8670552
Sum2934
Variance58.32335849
MonotonicityNot monotonic
2022-02-25T14:42:40.063479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
01264
55.9%
1313
 
13.8%
2119
 
5.3%
3104
 
4.6%
464
 
2.8%
542
 
1.9%
626
 
1.1%
718
 
0.8%
816
 
0.7%
1011
 
0.5%
Other values (18)41
 
1.8%
(Missing)245
 
10.8%
ValueCountFrequency (%)
01264
55.9%
1313
 
13.8%
2119
 
5.3%
3104
 
4.6%
464
 
2.8%
542
 
1.9%
626
 
1.1%
718
 
0.8%
816
 
0.7%
911
 
0.5%
ValueCountFrequency (%)
2401
< 0.1%
2041
< 0.1%
521
< 0.1%
421
< 0.1%
411
< 0.1%
401
< 0.1%
311
< 0.1%
281
< 0.1%
241
< 0.1%
221
< 0.1%

AGE
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)3.0%
Missing245
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean52.27353816
Minimum25
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:40.296762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile32
Q143.25
median52
Q361
95-th percentile71
Maximum86
Range61
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation11.86937262
Coefficient of variation (CV)0.2270627365
Kurtosis-0.6168068571
Mean52.27353816
Median Absolute Deviation (MAD)9
Skewness-0.06033047246
Sum105488
Variance140.8820064
MonotonicityNot monotonic
2022-02-25T14:42:40.522910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5567
 
3.0%
5266
 
2.9%
5863
 
2.8%
4662
 
2.7%
4862
 
2.7%
6161
 
2.7%
4460
 
2.7%
5960
 
2.7%
4359
 
2.6%
5657
 
2.5%
Other values (50)1401
61.9%
(Missing)245
 
10.8%
ValueCountFrequency (%)
256
 
0.3%
2610
 
0.4%
2710
 
0.4%
2814
0.6%
2913
0.6%
3015
0.7%
3120
0.9%
3223
1.0%
3329
1.3%
3417
0.8%
ValueCountFrequency (%)
862
 
0.1%
831
 
< 0.1%
822
 
0.1%
812
 
0.1%
801
 
< 0.1%
794
 
0.2%
7810
0.4%
778
0.4%
766
0.3%
757
0.3%

CATEGORY
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing245
Missing (%)10.8%
Memory size130.8 KiB
GENERAL
1392 
SC
383 
ST
243 

Length

Max length7
Median length7
Mean length5.448959366
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowST
3rd rowST
4th rowSC
5th rowSC

Common Values

ValueCountFrequency (%)
GENERAL1392
61.5%
SC383
 
16.9%
ST243
 
10.7%
(Missing)245
 
10.8%

Length

2022-02-25T14:42:41.037253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-25T14:42:41.173344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
general1392
69.0%
sc383
 
19.0%
st243
 
12.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.4%
Missing245
Missing (%)10.8%
Memory size143.5 KiB
Post Graduate
503 
Graduate
441 
Graduate Professional
336 
12th Pass
256 
10th Pass
196 
Other values (3)
286 

Length

Max length21
Median length10
Mean length11.85232904
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12th Pass
2nd rowPost Graduate
3rd row12th Pass
4th rowDoctorate
5th rowPost Graduate

Common Values

ValueCountFrequency (%)
Post Graduate503
22.2%
Graduate441
19.5%
Graduate Professional336
14.8%
12th Pass256
11.3%
10th Pass196
 
8.7%
Illiterate183
 
8.1%
Doctorate73
 
3.2%
Literate30
 
1.3%
(Missing)245
10.8%

Length

2022-02-25T14:42:41.342634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-25T14:42:41.491734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
graduate1280
38.7%
post503
 
15.2%
pass452
 
13.7%
professional336
 
10.2%
12th256
 
7.7%
10th196
 
5.9%
illiterate183
 
5.5%
doctorate73
 
2.2%
literate30
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GRADUATE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing242
Missing (%)10.7%
Memory size128.0 KiB
1.0
1346 
0.0
675 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01346
59.5%
0.0675
29.8%
(Missing)242
 
10.7%

Length

2022-02-25T14:42:41.751178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-25T14:42:41.884266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.01346
66.6%
0.0675
33.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASSETS
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1979
Distinct (%)98.1%
Missing245
Missing (%)10.8%
Memory size170.5 KiB
Not Available
 
22
Nil
 
3
Rs 2,17,99,19,870 ~ 217 Crore+
 
2
Rs 15,95,000 ~ 15 Lacs+
 
2
Rs 3,11,000 ~ 3 Lacs+
 
2
Other values (1974)
1987 

Length

Max length31
Median length26
Mean length25.55896928
Min length1

Characters and Unicode

Total characters1992
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1961 ?
Unique (%)97.2%

Sample

1st rowRs 30,99,414 ~ 30 Lacs+
2nd rowRs 1,84,77,888 ~ 1 Crore+
3rd rowRs 3,64,91,000 ~ 3 Crore+
4th rowRs 7,42,74,036 ~ 7 Crore+
5th rowRs 13,37,84,385 ~ 13 Crore+

Common Values

ValueCountFrequency (%)
Not Available22
 
1.0%
Nil3
 
0.1%
Rs 2,17,99,19,870 ~ 217 Crore+2
 
0.1%
Rs 15,95,000 ~ 15 Lacs+2
 
0.1%
Rs 3,11,000 ~ 3 Lacs+2
 
0.1%
Rs 3,00,000 ~ 3 Lacs+2
 
0.1%
Rs 47,000 ~ 47 Thou+2
 
0.1%
Rs 11,95,43,561 ~ 11 Crore+2
 
0.1%
Rs 6,80,000 ~ 6 Lacs+2
 
0.1%
Rs 88,10,000 ~ 88 Lacs+2
 
0.1%
Other values (1969)1977
87.4%
(Missing)245
 
10.8%

Length

2022-02-25T14:42:42.101409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rs1992
19.9%
1988
19.9%
crore1378
13.8%
lacs588
 
5.9%
1325
 
3.2%
2174
 
1.7%
4117
 
1.2%
3111
 
1.1%
595
 
0.9%
671
 
0.7%
Other values (2117)3164
31.6%

Most occurring characters

ValueCountFrequency (%)
1992
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1992
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1992
100.0%

LIABILITIES
Categorical

HIGH CARDINALITY
MISSING

Distinct1226
Distinct (%)60.8%
Missing245
Missing (%)10.8%
Memory size156.8 KiB
Rs 0 ~
634 
Not Available
 
22
Rs 5,00,000 ~ 5 Lacs+
 
10
Rs 50,000 ~ 50 Thou+
 
8
Rs 1,00,000 ~ 1 Lacs+
 
8
Other values (1221)
1336 

Length

Max length31
Median length22
Mean length18.59167493
Min length3

Characters and Unicode

Total characters1995
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1164 ?
Unique (%)57.7%

Sample

1st rowRs 2,31,450 ~ 2 Lacs+
2nd rowRs 8,47,000 ~ 8 Lacs+
3rd rowRs 1,53,00,000 ~ 1 Crore+
4th rowRs 86,06,522 ~ 86 Lacs+
5th rowRs 2,22,51,891 ~ 2 Crore+

Common Values

ValueCountFrequency (%)
Rs 0 ~634
28.0%
Not Available22
 
1.0%
Rs 5,00,000 ~ 5 Lacs+10
 
0.4%
Rs 50,000 ~ 50 Thou+8
 
0.4%
Rs 1,00,000 ~ 1 Lacs+8
 
0.4%
Rs 3,00,000 ~ 3 Lacs+7
 
0.3%
Rs 4,00,000 ~ 4 Lacs+6
 
0.3%
Rs 60,000 ~ 60 Thou+6
 
0.3%
Rs 12,00,000 ~ 12 Lacs+6
 
0.3%
Rs 6,00,000 ~ 6 Lacs+6
 
0.3%
Other values (1216)1305
57.7%
(Missing)245
 
10.8%

Length

2022-02-25T14:42:42.318554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rs1995
22.8%
1991
22.8%
lacs899
 
10.3%
0634
 
7.2%
crore391
 
4.5%
1165
 
1.9%
2101
 
1.2%
thou70
 
0.8%
369
 
0.8%
561
 
0.7%
Other values (1324)2372
27.1%

Most occurring characters

ValueCountFrequency (%)
1995
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1995
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1995
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1995
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1995
100.0%

GENERAL VOTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2244
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261599.0831
Minimum1339
Maximum1066824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:42.539688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1339
5-th percentile11764.3
Q121034.5
median153934
Q3485804
95-th percentile704952.7
Maximum1066824
Range1065485
Interquartile range (IQR)464769.5

Descriptive statistics

Standard deviation254990.5881
Coefficient of variation (CV)0.9747380804
Kurtosis-1.006782617
Mean261599.0831
Median Absolute Deviation (MAD)141693
Skewness0.5651866623
Sum591998725
Variance6.50202 × 1010
MonotonicityNot monotonic
2022-02-25T14:42:42.799859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119793
 
0.1%
137683
 
0.1%
5637152
 
0.1%
137152
 
0.1%
155502
 
0.1%
186392
 
0.1%
119602
 
0.1%
248922
 
0.1%
4297102
 
0.1%
112862
 
0.1%
Other values (2234)2241
99.0%
ValueCountFrequency (%)
13391
< 0.1%
13651
< 0.1%
14871
< 0.1%
16291
< 0.1%
22871
< 0.1%
24781
< 0.1%
28371
< 0.1%
29501
< 0.1%
36301
< 0.1%
39221
< 0.1%
ValueCountFrequency (%)
10668241
< 0.1%
10071561
< 0.1%
9799461
< 0.1%
9694301
< 0.1%
9425761
< 0.1%
9360651
< 0.1%
9184721
< 0.1%
9107871
< 0.1%
9094321
< 0.1%
8927441
< 0.1%

POSTAL VOTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1217
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990.7105612
Minimum0
Maximum19367
Zeros35
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:43.057302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q157
median316
Q31385
95-th percentile3915.8
Maximum19367
Range19367
Interquartile range (IQR)1328

Descriptive statistics

Standard deviation1602.839174
Coefficient of variation (CV)1.617868262
Kurtosis24.9886315
Mean990.7105612
Median Absolute Deviation (MAD)304
Skewness3.842707297
Sum2241978
Variance2569093.416
MonotonicityNot monotonic
2022-02-25T14:42:43.347493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035
 
1.5%
130
 
1.3%
328
 
1.2%
226
 
1.1%
520
 
0.9%
418
 
0.8%
717
 
0.8%
616
 
0.7%
1216
 
0.7%
2715
 
0.7%
Other values (1207)2042
90.2%
ValueCountFrequency (%)
035
1.5%
130
1.3%
226
1.1%
328
1.2%
418
0.8%
520
0.9%
616
0.7%
717
0.8%
812
 
0.5%
911
 
0.5%
ValueCountFrequency (%)
193671
< 0.1%
178521
< 0.1%
151691
< 0.1%
141961
< 0.1%
138801
< 0.1%
130851
< 0.1%
109741
< 0.1%
107721
< 0.1%
98581
< 0.1%
96211
< 0.1%

TOTAL VOTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2247
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262589.7936
Minimum1342
Maximum1068569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:43.618443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1342
5-th percentile11767
Q121162.5
median154489
Q3487231.5
95-th percentile706670.4
Maximum1068569
Range1067227
Interquartile range (IQR)466069

Descriptive statistics

Standard deviation255982.2356
Coefficient of variation (CV)0.9748369581
Kurtosis-1.006543464
Mean262589.7936
Median Absolute Deviation (MAD)142172
Skewness0.5656176046
Sum594240703
Variance6.552690497 × 1010
MonotonicityNot monotonic
2022-02-25T14:42:43.882620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146922
 
0.1%
99122
 
0.1%
151102
 
0.1%
126752
 
0.1%
183382
 
0.1%
133232
 
0.1%
160012
 
0.1%
199722
 
0.1%
4710522
 
0.1%
212412
 
0.1%
Other values (2237)2243
99.1%
ValueCountFrequency (%)
13421
< 0.1%
13691
< 0.1%
14871
< 0.1%
16461
< 0.1%
22871
< 0.1%
24861
< 0.1%
28391
< 0.1%
29501
< 0.1%
36301
< 0.1%
39901
< 0.1%
ValueCountFrequency (%)
10685691
< 0.1%
10089361
< 0.1%
9829421
< 0.1%
9727391
< 0.1%
9445031
< 0.1%
9381601
< 0.1%
9240651
< 0.1%
9132221
< 0.1%
9115941
< 0.1%
9001491
< 0.1%

OVER TOTAL ELECTORS IN CONSTITUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8114121
Minimum0.097941091
Maximum51.95101197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:44.129786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.097941091
5-th percentile0.7187977656
Q11.29651798
median10.51055288
Q329.46818457
95-th percentile40.92665089
Maximum51.95101197
Range51.85307088
Interquartile range (IQR)28.17166659

Descriptive statistics

Standard deviation14.96286096
Coefficient of variation (CV)0.9463329948
Kurtosis-1.250675211
Mean15.8114121
Median Absolute Deviation (MAD)9.688992897
Skewness0.4590096545
Sum35781.22558
Variance223.8872082
MonotonicityNot monotonic
2022-02-25T14:42:44.420978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.22849871
 
< 0.1%
25.867340851
 
< 0.1%
24.92450151
 
< 0.1%
21.883691991
 
< 0.1%
3.8470298231
 
< 0.1%
1.1010622861
 
< 0.1%
27.147561891
 
< 0.1%
31.424210811
 
< 0.1%
23.183513951
 
< 0.1%
1.7819560161
 
< 0.1%
Other values (2253)2253
99.6%
ValueCountFrequency (%)
0.0979410911
< 0.1%
0.1177582441
< 0.1%
0.3576970521
< 0.1%
0.3769338061
< 0.1%
0.4897551781
< 0.1%
0.4919533641
< 0.1%
0.4997024711
< 0.1%
0.5299582241
< 0.1%
0.5609443151
< 0.1%
0.5620688291
< 0.1%
ValueCountFrequency (%)
51.951011971
< 0.1%
51.456884281
< 0.1%
50.80912861
< 0.1%
50.503955641
< 0.1%
50.49739851
< 0.1%
50.184392071
< 0.1%
50.148058871
< 0.1%
50.114331311
< 0.1%
49.893907651
< 0.1%
49.336091761
< 0.1%

OVER TOTAL VOTES POLLED IN CONSTITUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.19052537
Minimum1.000039181
Maximum74.41185575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:44.661138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.000039181
5-th percentile1.091290906
Q11.899501697
median16.22172112
Q342.59023302
95-th percentile58.90678092
Maximum74.41185575
Range73.41181657
Interquartile range (IQR)40.69073132

Descriptive statistics

Standard deviation21.56475787
Coefficient of variation (CV)0.9298951845
Kurtosis-1.370190926
Mean23.19052537
Median Absolute Deviation (MAD)15.02974028
Skewness0.3885296835
Sum52480.15891
Variance465.0387818
MonotonicityNot monotonic
2022-02-25T14:42:44.902301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6305607071
 
< 0.1%
1.2862994231
 
< 0.1%
1.9463353481
 
< 0.1%
1.7902140051
 
< 0.1%
34.1863611
 
< 0.1%
1.2252144551
 
< 0.1%
1.5491487521
 
< 0.1%
41.777343851
 
< 0.1%
1.0702515851
 
< 0.1%
71.835341481
 
< 0.1%
Other values (2253)2253
99.6%
ValueCountFrequency (%)
1.0000391811
< 0.1%
1.0006885831
< 0.1%
1.0013203451
< 0.1%
1.0028639021
< 0.1%
1.0028901361
< 0.1%
1.0035697381
< 0.1%
1.0045853231
< 0.1%
1.0064707151
< 0.1%
1.0081003861
< 0.1%
1.0106610711
< 0.1%
ValueCountFrequency (%)
74.411855751
< 0.1%
74.298216671
< 0.1%
72.216433811
< 0.1%
71.835341481
< 0.1%
71.56004031
< 0.1%
71.384356171
< 0.1%
70.043197171
< 0.1%
69.57582131
< 0.1%
69.552354521
< 0.1%
69.329876551
< 0.1%

TOTAL ELECTORS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct542
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658015.947
Minimum55189
Maximum3150313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-02-25T14:42:45.163473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum55189
5-th percentile1259362.6
Q11530014
median1679030
Q31816857
95-th percentile2067463
Maximum3150313
Range3095124
Interquartile range (IQR)286843

Descriptive statistics

Standard deviation314518.6732
Coefficient of variation (CV)0.1896958071
Kurtosis6.988403622
Mean1658015.947
Median Absolute Deviation (MAD)144774
Skewness-1.273786459
Sum3752090087
Variance9.892199577 × 1010
MonotonicityNot monotonic
2022-02-25T14:42:45.569744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170459612
 
0.5%
174288310
 
0.4%
16123009
 
0.4%
14243858
 
0.4%
16862158
 
0.4%
15884548
 
0.4%
14343848
 
0.4%
13177388
 
0.4%
13791228
 
0.4%
16906208
 
0.4%
Other values (532)2176
96.2%
ValueCountFrequency (%)
551893
0.1%
1217404
0.2%
1792324
0.2%
2500297
0.3%
3184715
0.2%
3397886
0.3%
4341284
0.2%
4637755
0.2%
5567614
0.2%
5793524
0.2%
ValueCountFrequency (%)
31503135
0.2%
28492502
 
0.1%
27289783
0.1%
24974583
0.1%
24431123
0.1%
23789953
0.1%
23716443
0.1%
23709034
0.2%
23505802
 
0.1%
23029603
0.1%

Interactions

2022-02-25T14:42:32.362484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:08.902958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:12.246186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:15.226174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:18.455326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:21.581410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:24.825572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:27.430309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:29.837801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:32.583634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:09.585413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:12.464332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:15.563399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:18.674472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:21.955661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:25.172804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:27.695488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:30.084967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:33.062952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:09.984689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:12.683478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:15.822571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:18.910629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:22.285880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:25.445986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:27.921637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:30.359150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:33.334132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:10.445987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:12.914634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:16.196820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:19.149788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:22.587081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:25.760195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:28.146787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:30.650344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:33.574292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:10.820236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:13.162798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:16.865266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:19.443985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:22.998355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:26.028375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:28.382020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:31.001577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:33.833465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:11.231510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:13.415967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:17.280545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:19.757194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:23.384614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:26.456660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:28.697232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:31.306783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:34.073625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:11.469676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:13.900293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:17.552724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:20.108428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:23.709829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:26.691819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:29.015442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:31.553947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:34.352814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:11.698824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:14.553725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:17.865934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:20.443652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:24.133113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:26.922971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:29.246408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:31.798108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:34.607983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:11.943985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:14.922972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:18.200156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:21.162134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:24.492351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:27.176140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:29.558616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-02-25T14:42:32.110316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-02-25T14:42:45.837398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-25T14:42:46.292702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-25T14:42:47.048206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-25T14:42:47.460480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-25T14:42:47.740667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-25T14:42:35.091304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-25T14:42:36.013916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-25T14:42:36.548311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-25T14:42:37.029714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Sl No:STATECONSTITUENCYNAMEWINNERPARTYSYMBOLGENDERcriminalAGECATEGORYEDUCATIONGRADUATEASSETSLIABILITIESGENERAL VOTESPOSTAL VOTESTOTAL VOTESOVER TOTAL ELECTORS IN CONSTITUENCYOVER TOTAL VOTES POLLED IN CONSTITUENCYTOTAL ELECTORS
01TelanganaADILABADSOYAM BAPU RAO1BJPLotusMALE52.052.0ST12th Pass0.0Rs 30,99,414\n ~ 30 Lacs+Rs 2,31,450\n ~ 2 Lacs+37689248237737425.33068435.4682481489790
12TelanganaADILABADGodam Nagesh0TRSCarMALE0.054.0STPost Graduate1.0Rs 1,84,77,888\n ~ 1 Crore+Rs 8,47,000\n ~ 8 Lacs+31866514931881421.39992929.9643701489790
23TelanganaADILABADRATHOD RAMESH0INCHandMALE3.052.0ST12th Pass0.0Rs 3,64,91,000\n ~ 3 Crore+Rs 1,53,00,000\n ~ 1 Crore+31405718131423821.09277129.5342851489790
34TelanganaADILABADNOTA0NOTANaNNaNNaNNaNNaNNaNNaNNaNNaN130306130360.8750231.2252141489790
45Uttar PradeshAGRASatyapal Singh Baghel1BJPLotusMALE5.058.0SCDoctorate1.0Rs 7,42,74,036\n ~ 7 Crore+Rs 86,06,522\n ~ 86 Lacs+644459241664687533.38382356.4646151937690
56Uttar PradeshAGRAManoj Kumar Soni0BSPElephantMALE0.047.0SCPost Graduate1.0Rs 13,37,84,385\n ~ 13 Crore+Rs 2,22,51,891\n ~ 2 Crore+434199113043532922.46639037.9991251937690
67Uttar PradeshAGRAPreeta Harit0INCHandFEMALE0.054.0SCPost Graduate1.0Rs 5,50,75,753\n ~ 5 Crore+Rs 0\n ~44877272451492.3300423.9409791937690
78MaharashtraAHMADNAGARDr. SUJAY RADHAKRISHNA VIKHEPATIL1BJPLotusMALE0.037.0GENERALDoctorate1.0Rs 16,86,64,576\n ~ 16 Crore+Rs 26,23,964\n ~ 26 Lacs+696961769970466037.85653358.4251591861396
89MaharashtraAHMADNAGARSANGRAM ARUNKAKA JAGTAP0NCPClockMALE1.034.0GENERALGraduate1.0Rs 9,44,88,381\n ~ 9 Crore+Rs 3,25,49,132\n ~ 3 Crore+419364382242318622.73487235.0874311861396
910MaharashtraAHMADNAGARSUDHAKAR LAXMAN AVHAD0VBACup & SaucerMALE0.062.0GENERALGraduate1.0Rs 1,39,49,000\n ~ 1 Crore+Rs 14,00,000\n ~ 14 Lacs+31644163318071.7087712.6372001861396

Last rows

Sl No:STATECONSTITUENCYNAMEWINNERPARTYSYMBOLGENDERcriminalAGECATEGORYEDUCATIONGRADUATEASSETSLIABILITIESGENERAL VOTESPOSTAL VOTESTOTAL VOTESOVER TOTAL ELECTORS IN CONSTITUENCYOVER TOTAL VOTES POLLED IN CONSTITUENCYTOTAL ELECTORS
22532254MaharashtraYAVATMAL-WASHIMBhavana Pundlikrao Gawali1SHSBow and ArrowFEMALE3.046.0GENERALGraduate1.0Rs 9,68,73,189\n ~ 9 Crore+Rs 73,96,250\n ~ 73 Lacs+540104199454209828.29048346.1429121916185
22542255MaharashtraYAVATMAL-WASHIMThakre Manikrao Govindrao0INCHandMALE0.064.0GENERAL12th Pass0.0Rs 3,03,03,524\n ~ 3 Crore+Rs 12,92,750\n ~ 12 Lacs+422497166242415922.13559836.1040461916185
22552256MaharashtraYAVATMAL-WASHIMPravin Govind Pawar0VBACup & SaucerMALE0.037.0GENERALGraduate Professional1.0Rs 4,68,66,958\n ~ 4 Crore+Rs 6,58,47,040\n ~ 6 Crore+93918310942284.9174798.0206061916185
22562257MaharashtraYAVATMAL-WASHIMParashram Bhaosing Ade0INDTractor Chalata KisanMALE0.064.0GENERALPost Graduate1.0Rs 18,73,01,769\n ~ 18 Crore+Rs 4,17,919\n ~ 4 Lacs+2444752244991.2785302.0853341916185
22572258MaharashtraYAVATMAL-WASHIMVaishali Sudhakar Yede0PHJSPWhistleFEMALE0.028.0GENERAL10th Pass0.0Rs 11,68,500\n ~ 11 Lacs+Rs 9,000\n ~ 9 Thou+2056357206201.0760971.7551571916185
22582259MaharashtraYAVATMAL-WASHIMAnil Jayram Rathod0INDSHIPMALE0.043.0GENERALPost Graduate1.0Rs 48,90,000\n ~ 48 Lacs+Rs 10,20,000\n ~ 10 Lacs+1466125146860.7664191.2500601916185
22592260TelanganaZAHIRABADB.B.PATIL1TRSCarMALE18.063.0GENERALGraduate1.0Rs 1,28,78,51,556\n ~ 128 Crore+Rs 1,15,35,000\n ~ 1 Crore+43406617843424428.97536941.5741831498666
22602261TelanganaZAHIRABADMADAN MOHAN RAO0INCHandMALE0.049.0GENERALPost Graduate1.0Rs 90,36,63,001\n ~ 90 Crore+Rs 0\n ~42790011542801528.55973240.9778231498666
22612262TelanganaZAHIRABADBANALA LAXMA REDDY0BJPLotusMALE3.047.0GENERAL12th Pass0.0Rs 5,85,77,327\n ~ 5 Crore+Rs 52,50,000\n ~ 52 Lacs+1387312161389479.27137913.3026781498666
22622263TelanganaZAHIRABADNOTA0NOTANaNNaNNaNNaNNaNNaNNaNNaNNaN111382111400.7433281.0665351498666